NPR Story

Orbitz Targets Mac Users For Pricier Hotels

You know the ads that poke fun at the hapless, square PC compared with the hip and clever Mac?

(SOUNDBITE OF AN AD)

UNIDENTIFIED MAN #1: Hello, I'm a Mac.

UNIDENTIFIED MAN #2: And I'm a PC. And I feel inadequate.

BLOCK: Well, Mac users, along with supposedly being hip and clever, spend more money than PC users - at least on hotel rooms - substantially more. And the travel website Orbitz is funneling that data into its search engine, showing Mac users different, at times higher, priced options than it does PC users.

Dana Mattioli has been writing about this in the Wall Street Journal. And, Dana, let's talk about what you found. Orbitz says that Mac users spend more per night on hotel rooms. How much more?

DANA MATTIOLI: That's right. So they spend anywhere from $20 to $30 more a night, which is kind of significant considering the average booking on the site is about $100.

BLOCK: And how did Orbitz find this out?

MATTIOLI: So they had a hunch that Apple users and PC users acted differently. And then there are other's commercials that you just referenced before that kind of got them going and thinking about this. But then they had to go through, you know, hundreds and thousands of transactions on the site to see if this panned out in spending activity. And, lo and behold, it did. That wasn't the only finding.

They also saw that a Mac user is 40 percent more likely to book a four or five-star hotel. They also tend to stay in bigger or more expensive rooms if they stay at the same hotel as a PC user, and they opt for upgrades.

BLOCK: There's also some really interesting data out there about household income for people who use Macs versus people who use PCs. What does it show?

MATTIOLI: That's right. So, the average household income for an adult Mac user is $98,000. And that's compared to $74,000 for the PC owner. They also tend to be younger, higher education grade. They are - skew more male, actually. So there are these differences that could be meaningful to a retailer who's trying to get as much of an incremental spend as they can.

BLOCK: So, if I'm going to the Orbitz website from a Mac looking for a hotel room, what am I going to see that's different from someone who's going there from a PC?

MATTIOLI: Right, so we tested this a lot.

(LAUGHTER)

MATTIOLI: And if you were to search for a hotel in Miami, for instance, from a Mac and a PC at the same time on the same date, you'll see that some of the hotels are exactly the same from site to site but they'll be more higher end, more expensive hotels on the Mac side in the top 10 results, for instance. And there might be some hotels that don't appear on the PC side that appear on the Mac that are those more expensive hotels, like the Beaumont(ph) in South Beach.

BLOCK: And to clarify, Orbitz says they're not showing the same room to different users at different prices. They're showing different hotel options.

MATTIOLI: That's right. They're not trying to charge the Mac more. They are just giving different options. And then, you know, if you don't like those options, you could still find the lower end or the lower-cost hotels.

BLOCK: You know, it wouldn't have occurred to me, Dana, that a website would know that I'm checking from a Mac versus a PC. But I guess I shouldn't be surprised to find that out.

MATTIOLI: Yeah, websites know more about people than they understand. So that's one of the things that they instantly know about you when you come in. They know your IP address. They know approximate location. Also, you know, retailers in general have been doing predictive analytics and data mining, it's called. And they are trying to use loyalty cards, credit card programs, online cookies to take that information and exploit it.

So, they want to know if you normally shop sale versus full price; if you tend to buy high heels versus flats. And then, they're going to take that info and target you with it.

BLOCK: And the travel website here, Orbitz, is not making a secret out of this, Dana. They're quite honest about the fact that they're doing this data mining and that they're using it this way, right?

MATTIOLI: Yeah, this is something that the CEO is really proud of, actually. He is huge on predictive analytics. He became CEO in 2009. He came from KAYAK, which is another site like this. And he set up a team to really head up this program, and that's when they started making headway.

But it's important to keep in mind also that this isn't the only determinant of what hotels you're going to see. They look at your return visits. If you tend to look at the same property over and over, that might come up high in the search. If there is a deal on a hotel, that's going to come up high in both searches 'cause they want to show you a good deal. So there's a number of factors that come into play.